class_docstr: Classifier model for nodes.
code: def __init__(
      self,
      seq_length,
      num_classes,
      hidden_units=None,
      dropout_rate=0.5,
      **kwargs,
  ):
      inputs = tf.keras.Input(
          shape=(seq_length,), dtype=tf.int64, name="words"
      )
      x = tf.keras.layers.Lambda(lambda x: tf.cast(x, tf.float32))(
          inputs
      )
      for num_units in hidden_units or [50, 50]:
          x = tf.keras.layers.Dense(num_units, activation="relu")(x)
          x = tf.keras.layers.Dropout(dropout_rate)(x)
      outputs = tf.keras.layers.Dense(num_classes)(x)
      super(NodeClassifier, self).__init__(inputs, outputs, **kwargs)
  
file_path: neural-structured-learning/neural_structured_learning/examples/graph_nets_cora_graph_regularization.py
imports: import functools
  from absl import app
  from absl import flags
  import graph_nets
  import neural_structured_learning
  from neural_structured_learning.experimental import gnn
  import tensorflow
label_desc: Use image node_id 1 to create a new NodeClassifier object from inherited base classes: tf.keras with example: obj = NodeClassifier(seq_length, num_classes, hidden_units, dropout_rate)